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bioRxiv · 10.1101/2021.04.22.440949

DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics

Gao, M.·Yang, W.·Li, C.·Chang, Y.·Liu, Y.·Wang, S.·He, Q.·Zhong, C.-Q.·Shuai, J.·Yu, R.·Han, J.
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Abstract

We developed DreamDIA-XMBD, a software suite for data-independent acquisition (DIA) data analysis. DreamDIA-XMBD adopts a data-driven strategy to capture comprehensive information from elution patterns of target peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. More specifically, in contrast to existing methods which use only 6 to 10 selected transitions from spectral library, DreamDIA-XMBD extracts additional features from dozens of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by non-linear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA-XMBD is written in Python, and is publicly available at https://github.com/xmuyulab/Dream-DIA-XMBD for high coverage and precision DIA data analysis.

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Gao, M., Yang, W., Li, C., Chang, Y., Liu, Y., Wang, S., He, Q., Zhong, C.-Q., Shuai, J., Yu, R., Han, J.. 2021-04-23. DreamDIA-XMBD: deep representation features improve the analysis of data-independent acquisition proteomics. https://doi.org/10.1101/2021.04.22.440949

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